Carbon emission flow from self-driving tours and its spatial relationship with scenic spots - a traffic-related big data method.

Tour activities are closely related to carbon emissions. Low-carbon tourism is an important means of saving energy and reducing emissions. With the advent of big data mining technology, it is urgent foracademia to discuss the measurement of self-driving tour carbon emission flows and its spatial relationship with scenic spots based on big data on traffic. This paper measures self-driving tour carbon emission flow data from 2014 and analyzes its spatial relationship with the scenic spots using data mining technology and the tour traffic carbon emission flow analysis method. Results show that (1) the regions that have high expressway traffic and self-driving tour traffic are mostly concentrated along the Yangtze River, while the regions that have low expressway traffic and self-driving tour traffic are concentrated in north Jiangsu. For regions that have high self-driving tour traffic, they are spatially concentrated in Nanjing, Suzhou, Wuxi and Changzhou. (2) The self-driving tour carbon emission flow totals 0.52 Mt in Jiangsu, the top five carbon emitters are the routes from downtown Nanjing to downtown Nanjing, downtown Suzhou to downtown Suzhou, downtown Suzhou to downtown Wuxi, downtown Changzhou to downtown Nanjing, and downtown Suzhou to downtown Nanjing. The total number of carbon emissions from these routes account for 17.25%, making them a major source of carbon emissions in Jiangsu. (3) Carbon emissions from self-driving tours do not have a significant positive correlation with the grades of the scenic spots. That is, high carbon emission flows from self-driving tours may happen to both high-grade and low-grade scenic spots.

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